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math.OC2026
Adjoint Matching through the Lens of the Stochastic Maximum Principle in Optimal Control
Carles Domingo-Enrich, Jiequn Han
Reward fine-tuning of diffusion and flow models and sampling from tilted or Boltzmann distributions can both be formulated as stochastic optimal control (SOC) problems, where learn…
math.OC2025
Self-Supervised Amortized Neural Operators for Optimal Control: Scaling Laws and Applications
Wuzhe Xu, Jiequn Han, Rongjie Lai
Optimal control provides a principled framework for transforming dynamical system models into intelligent decision-making, yet classical computational approaches are often too expe…
math.OC2023
Stochastic Optimal Control Matching
Carles Domingo-Enrich, Jiequn Han, Brandon Amos +2
Stochastic optimal control, which has the goal of driving the behavior of noisy systems, is broadly applicable in science, engineering and artificial intelligence. Our work introdu…